Overview

Dataset statistics

Number of variables7
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.8 KiB
Average record size in memory56.1 B

Variable types

Numeric6
Categorical1

Alerts

X1 is highly overall correlated with X4 and 1 other fieldsHigh correlation
X2 is highly overall correlated with X4 and 2 other fieldsHigh correlation
X4 is highly overall correlated with X1 and 2 other fieldsHigh correlation
X5 is highly overall correlated with X1 and 1 other fieldsHigh correlation
X6 is highly overall correlated with X2 and 2 other fieldsHigh correlation
y is highly overall correlated with X2 and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-07-06 13:47:26.161168
Analysis finished2024-07-06 13:47:38.176947
Duration12.02 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

X1
Real number (ℝ)

HIGH CORRELATION 

Distinct882
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.90022
Minimum-0.436
Maximum7.356
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2024-07-06T13:47:38.389042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.436
5-th percentile2.39495
Q12.867
median3.666
Q34.93625
95-th percentile5.8554
Maximum7.356
Range7.792
Interquartile range (IQR)2.06925

Descriptive statistics

Standard deviation1.2249992
Coefficient of variation (CV)0.31408463
Kurtosis-0.7651691
Mean3.90022
Median Absolute Deviation (MAD)0.9665
Skewness0.20911485
Sum3900.22
Variance1.5006229
MonotonicityNot monotonic
2024-07-06T13:47:38.694036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.897 4
 
0.4%
3.201 4
 
0.4%
3.167 3
 
0.3%
2.888 3
 
0.3%
3.355 3
 
0.3%
2.918 3
 
0.3%
2.691 3
 
0.3%
2.816 3
 
0.3%
2.784 3
 
0.3%
4.673 3
 
0.3%
Other values (872) 968
96.8%
ValueCountFrequency (%)
-0.436 1
0.1%
0.096 1
0.1%
0.508 1
0.1%
0.783 1
0.1%
0.794 1
0.1%
0.83 1
0.1%
1.387 1
0.1%
1.46 1
0.1%
1.517 1
0.1%
1.692 1
0.1%
ValueCountFrequency (%)
7.356 1
0.1%
7.265 1
0.1%
7.21 1
0.1%
7.178 1
0.1%
6.983 1
0.1%
6.786 1
0.1%
6.616 1
0.1%
6.56 1
0.1%
6.551 1
0.1%
6.464 1
0.1%

X2
Real number (ℝ)

HIGH CORRELATION 

Distinct905
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.461672
Minimum0.585
Maximum11.225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-06T13:47:38.988874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.585
5-th percentile2.0637
Q13.44175
median4.3375
Q35.366
95-th percentile7.2396
Maximum11.225
Range10.64
Interquartile range (IQR)1.92425

Descriptive statistics

Standard deviation1.5381207
Coefficient of variation (CV)0.34474088
Kurtosis0.28856222
Mean4.461672
Median Absolute Deviation (MAD)0.964
Skewness0.48742585
Sum4461.672
Variance2.3658154
MonotonicityNot monotonic
2024-07-06T13:47:39.308299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.462 3
 
0.3%
4.509 3
 
0.3%
4.115 3
 
0.3%
3.457 3
 
0.3%
4.801 3
 
0.3%
3.975 2
 
0.2%
6.766 2
 
0.2%
4.243 2
 
0.2%
3.946 2
 
0.2%
5.098 2
 
0.2%
Other values (895) 975
97.5%
ValueCountFrequency (%)
0.585 1
0.1%
1.27 1
0.1%
1.369 1
0.1%
1.442 1
0.1%
1.466 1
0.1%
1.506 1
0.1%
1.524 1
0.1%
1.59 1
0.1%
1.64 1
0.1%
1.666 1
0.1%
ValueCountFrequency (%)
11.225 1
0.1%
10.72 1
0.1%
9.175 1
0.1%
9.115 1
0.1%
8.833 1
0.1%
8.754 1
0.1%
8.659 1
0.1%
8.624 1
0.1%
8.593 1
0.1%
8.475 1
0.1%

X3
Real number (ℝ)

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.548
Minimum6
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-07-06T13:47:39.544795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9
Q111
median13
Q314
95-th percentile16
Maximum19
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0498531
Coefficient of variation (CV)0.16336095
Kurtosis-0.053380111
Mean12.548
Median Absolute Deviation (MAD)1
Skewness-0.0078694849
Sum12548
Variance4.2018979
MonotonicityNot monotonic
2024-07-06T13:47:39.795906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
12 185
18.5%
14 182
18.2%
13 171
17.1%
11 142
14.2%
10 97
9.7%
15 85
8.5%
16 48
 
4.8%
9 42
 
4.2%
8 20
 
2.0%
17 17
 
1.7%
Other values (4) 11
 
1.1%
ValueCountFrequency (%)
6 2
 
0.2%
7 2
 
0.2%
8 20
 
2.0%
9 42
 
4.2%
10 97
9.7%
11 142
14.2%
12 185
18.5%
13 171
17.1%
14 182
18.2%
15 85
8.5%
ValueCountFrequency (%)
19 3
 
0.3%
18 4
 
0.4%
17 17
 
1.7%
16 48
 
4.8%
15 85
8.5%
14 182
18.2%
13 171
17.1%
12 185
18.5%
11 142
14.2%
10 97
9.7%

X4
Real number (ℝ)

HIGH CORRELATION 

Distinct872
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.641536
Minimum-0.433
Maximum5.965
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.2%
Memory size7.9 KiB
2024-07-06T13:47:40.077360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.433
5-th percentile1.7449
Q12.90775
median3.8325
Q34.3985
95-th percentile5.19475
Maximum5.965
Range6.398
Interquartile range (IQR)1.49075

Descriptive statistics

Standard deviation1.0556846
Coefficient of variation (CV)0.2899009
Kurtosis-0.19011269
Mean3.641536
Median Absolute Deviation (MAD)0.648
Skewness-0.49392676
Sum3641.536
Variance1.1144699
MonotonicityNot monotonic
2024-07-06T13:47:40.367861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.47 4
 
0.4%
3.93 3
 
0.3%
4.554 3
 
0.3%
3.593 3
 
0.3%
4.263 3
 
0.3%
2.497 3
 
0.3%
4.223 3
 
0.3%
4.208 3
 
0.3%
4.231 3
 
0.3%
4.443 3
 
0.3%
Other values (862) 969
96.9%
ValueCountFrequency (%)
-0.433 1
0.1%
-0.121 1
0.1%
0.695 1
0.1%
0.725 1
0.1%
0.87 1
0.1%
0.914 1
0.1%
0.956 1
0.1%
0.99 1
0.1%
1.001 1
0.1%
1.053 1
0.1%
ValueCountFrequency (%)
5.965 1
0.1%
5.915 1
0.1%
5.899 1
0.1%
5.862 1
0.1%
5.847 1
0.1%
5.712 1
0.1%
5.696 1
0.1%
5.643 1
0.1%
5.615 1
0.1%
5.609 1
0.1%

X5
Real number (ℝ)

HIGH CORRELATION 

Distinct853
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.100318
Minimum-1.306
Maximum7.638
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.3%
Memory size7.9 KiB
2024-07-06T13:47:40.666076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.306
5-th percentile2.49245
Q13.393
median3.7915
Q34.95025
95-th percentile6.0115
Maximum7.638
Range8.944
Interquartile range (IQR)1.55725

Descriptive statistics

Standard deviation1.1465952
Coefficient of variation (CV)0.27963568
Kurtosis0.8509969
Mean4.100318
Median Absolute Deviation (MAD)0.611
Skewness0.038444717
Sum4100.318
Variance1.3146806
MonotonicityNot monotonic
2024-07-06T13:47:40.945626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.732 5
 
0.5%
3.461 4
 
0.4%
3.271 4
 
0.4%
3.686 4
 
0.4%
3.255 3
 
0.3%
3.431 3
 
0.3%
5.482 3
 
0.3%
3.656 3
 
0.3%
3.457 3
 
0.3%
3.408 3
 
0.3%
Other values (843) 965
96.5%
ValueCountFrequency (%)
-1.306 1
0.1%
-0.528 1
0.1%
-0.134 1
0.1%
0.134 1
0.1%
0.152 1
0.1%
0.354 1
0.1%
0.868 1
0.1%
1.055 1
0.1%
1.217 1
0.1%
1.359 1
0.1%
ValueCountFrequency (%)
7.638 1
0.1%
7.434 1
0.1%
7.39 1
0.1%
7.23 1
0.1%
7.198 1
0.1%
6.975 1
0.1%
6.928 1
0.1%
6.896 1
0.1%
6.89 1
0.1%
6.848 1
0.1%

X6
Real number (ℝ)

HIGH CORRELATION 

Distinct924
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.315121
Minimum-2.252
Maximum9.796
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)1.0%
Memory size7.9 KiB
2024-07-06T13:47:41.224497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.252
5-th percentile0.99195
Q12.2695
median3.21
Q34.35925
95-th percentile5.88085
Maximum9.796
Range12.048
Interquartile range (IQR)2.08975

Descriptive statistics

Standard deviation1.5522445
Coefficient of variation (CV)0.46823163
Kurtosis0.40636588
Mean3.315121
Median Absolute Deviation (MAD)1.0645
Skewness0.23692111
Sum3315.121
Variance2.409463
MonotonicityNot monotonic
2024-07-06T13:47:41.535716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.066 3
 
0.3%
5.955 2
 
0.2%
1.49 2
 
0.2%
3.124 2
 
0.2%
2.7 2
 
0.2%
5.715 2
 
0.2%
2.348 2
 
0.2%
1.03 2
 
0.2%
3.336 2
 
0.2%
1.779 2
 
0.2%
Other values (914) 979
97.9%
ValueCountFrequency (%)
-2.252 1
0.1%
-1.85 1
0.1%
-1.594 1
0.1%
-1.328 1
0.1%
-0.402 1
0.1%
-0.36 1
0.1%
-0.343 1
0.1%
-0.228 1
0.1%
-0.072 1
0.1%
-0.001 1
0.1%
ValueCountFrequency (%)
9.796 1
0.1%
8.668 1
0.1%
8.401 1
0.1%
8.21 1
0.1%
8.18 1
0.1%
7.668 1
0.1%
7.606 1
0.1%
7.586 1
0.1%
7.46 1
0.1%
7.381 1
0.1%

y
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
799 
1
201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 799
79.9%
1 201
 
20.1%

Length

2024-07-06T13:47:41.807228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T13:47:42.032802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 799
79.9%
1 201
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 799
79.9%
1 201
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 799
79.9%
1 201
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 799
79.9%
1 201
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 799
79.9%
1 201
 
20.1%

Interactions

2024-07-06T13:47:35.839060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:26.499366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:28.552907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:30.324694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:32.638243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:34.342301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:36.109716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:26.777133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:28.813587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:30.707523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:33.029175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:34.598712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:36.385468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:27.022910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:29.037645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:31.079500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:33.287373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:34.833097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:36.639727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:27.295455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:29.301947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:31.472820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:33.535757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:35.080595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:36.891150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:27.586767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:29.573427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:31.823328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:33.782891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:35.361072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:37.133564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:28.298381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:29.946604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:32.219179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:34.055942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-06T13:47:35.598912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-06T13:47:42.205857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
X1X2X3X4X5X6y
X11.000-0.363-0.0130.8280.776-0.0900.303
X2-0.3631.000-0.004-0.7850.143-0.8530.596
X3-0.013-0.0041.000-0.008-0.0160.0090.063
X40.828-0.785-0.0081.0000.4540.3880.578
X50.7760.143-0.0160.4541.000-0.6080.458
X6-0.090-0.8530.0090.388-0.6081.0000.574
y0.3030.5960.0630.5780.4580.5741.000

Missing values

2024-07-06T13:47:37.480866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-06T13:47:38.035625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

X1X2X3X4X5X6y
03.8573.065124.2643.5604.8010
14.9772.168145.2094.3434.9191
22.9635.089102.9073.4013.3440
33.1493.715133.6283.0954.6400
46.0474.877144.4706.3611.3200
55.6845.253144.1256.1381.1920
64.8992.171135.1714.2674.9731
72.6866.721132.0243.7101.8470
84.8944.136164.2644.9622.9310
93.2963.189133.9403.0525.0800
X1X2X3X4X5X6y
9902.6097.382111.6843.8701.2150
9912.3663.281103.4562.1705.6621
9922.7845.74262.5223.4582.7950
9932.7936.041112.3883.5732.4780
9946.0942.545155.5665.5773.7131
9951.7903.206103.2171.5766.1591
9962.9525.203132.8503.4313.2330
9973.3422.858134.1152.9805.3910
9985.2682.270125.3014.6664.6011
9995.7662.424165.4665.2114.0781